The scipy.optimize package provides several commonly used optimization algorithms. A detailed listing is available: scipy.optimize (can also be found by help(scipy.optimize)).

It includes solvers for nonlinear problems (with support for both local and global optimization algorithms), linear programming, constrained and nonlinear least-squares, root finding, and …

Differential evolution is a stochastic population based method that is useful for global optimization problems. At each pass through the population the algorithm mutates each candidate solution …

This can be used to print or plot the optimization results at each step, and to stop the optimization algorithm based on some user-defined condition. Only implemented for the trf and dogbox …

Method trust-constr is a trust-region algorithm for constrained optimization. It switches between two implementations depending on the problem definition. It is the most versatile constrained …

DIviding RECTangles (DIRECT) is a deterministic global optimization algorithm capable of minimizing a black box function with its variables subject to lower and upper bound constraints …

It should be easy to understand the algorithm just by reading our code. Our code diverges a bit from standard presentations: we choose a different formula for the extrapolation step.

SciPy API Optimization and root finding (scipy.optimize) minimize (method=’Nelder-Mead’)

Added in version 1.3.0. Method ‘simplex’ uses a traditional, full-tableau implementation of Dantzig’s simplex algorithm [1], [2] (not the Nelder-Mead simplex). This algorithm is included …

curve_fit is for local optimization of parameters to minimize the sum of squares of residuals. For global optimization, other choices of objective function, and other advanced features, consider …